基于高光谱成像技术的油菜苗期光照胁迫诊断

doi:10.3785/j.issn.1008-9209.2021.03.081

浙江大学学报（农业与生命科学版）

2022, Vol. 48

Issue (1): 106-116 DOI: 10.3785/j.issn.1008-9209.2021.03.081

农业工程

基于高光谱成像技术的油菜苗期光照胁迫诊断

王怡田^1,²(

),张小敏^1,²(

),姜海益³,张延宁^1,²,林洋洋^1,²,饶秀勤^1,²(

)

^1.浙江大学生物系统工程与食品科学学院, 杭州 310058
^2.农业农村部农产品产地处理装备重点实验室, 杭州 310058
^3.浙江大学数学科学学院, 杭州 310058

Light stress diagnosis of rapeseed seedling stage based on hyperspectral imaging technology

Yitian WANG^1,²(

),Xiaomin ZHANG^1,²(

),Haiyi JIANG³,Yanning ZHANG^1,²,Yangyang LIN^1,²,Xiuqin RAO^1,²(

)

^1.College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
^2.Key Laboratory of On-Site Processing Equipment for Agricultural Products, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
^3.School of Mathematical Sciences, Zhejiang University, Hangzhou 310058, China

全文: PDF(3103 KB) HTML

摘要：

为实现油菜苗早期光照胁迫诊断，利用高光谱成像技术对进入两叶一心阶段的油菜苗进行为期21 d的光照胁迫实验，对采集到的冠层叶片光谱进行预处理后，通过光谱反射率和连续小波变换提取光照胁迫敏感波段，然后分别采用连续投影算法和连续小波变换-逐步判别分析法提取特征波长和小波特征。为进一步提升光照胁迫检测模型的准确率，通过分析油菜苗期光谱波段特征及其随时间的演化规律，筛选出939～978 nm波段曲线面积、特征角正切值tan θ以及984和1 408 nm处反射率共4个特征，建立多特征融合的光照胁迫Fisher判别模型。结果表明，该模型的平均分类准确率为86.88%，在d₂₀族达到最佳分类效果，准确率为95.00%。本研究为后续基于高光谱成像技术的油菜光照胁迫快速诊断方法提供了有力的参考。

关键词： 油菜; 光照胁迫; 高光谱成像; 特征融合

Abstract:

Light stress can restrict the normal growth and development of rapeseed seedlings. In order to realize the early diagnosis of light stress in rapeseed seedlings, a 21 d experiment was conducted on rapeseed seedlings of two leaves and one heart stage using hyperspectral imaging technology. After preprocessing the collected canopy leaf spectra, the light-stress-sensitive bands were extracted through spectral reflectance and continuous wavelet transform. Then successive projection algorithm was used to extract characteristic wavelengths, and the continuous wavelet transform-stepwise discriminant analysis method was used to extract wavelet features. To further improve the accuracy of the stress detection model, a total of four features including the area under curvein the 939-978 nm band, the tangent value of the characteristic angle (tan θ), the reflectances at 984 and 1 408 nm were selected by analyzing the characteristics of the spectral band and the evolution of the spectral characteristics at the seedling stage of rapeseed to establish a multi-feature fusion Fisher discriminant model. The results showed that the average classification accuracy of the model was 86.88%, which achieved the best classification effect in the d₂₀ family, with an accuracy of 95.00%. The research provides a powerful reference for the rapid diagnosis of light stress in rapeseed based on hyperspectral imaging technology.

Key words: rapeseed light stress hyperspectral imaging feature fusion

收稿日期: 2021-03-08 出版日期: 2022-03-04

CLC:

S 24

基金资助: 国家重点研发计划(2017YFD0700800)

通讯作者: 饶秀勤 E-mail: 21713018@zju.edu.cn;xqrao@zju.edu.cn

作者简介: 王怡田（https://orcid.org/0000-0001-8179-8541），E-mail：21713018@zju.edu.cn|王怡田（https://orcid.org/0000-0001-8179-8541），E-mail：21713018@zju.edu.cn

	服务
	把本文推荐给朋友
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	王怡田
	张小敏
	姜海益
	张延宁
	林洋洋
	饶秀勤

引用本文:

王怡田,张小敏,姜海益,张延宁,林洋洋,饶秀勤. 基于高光谱成像技术的油菜苗期光照胁迫诊断[J]. 浙江大学学报（农业与生命科学版）, 2022, 48(1): 106-116.

Yitian WANG,Xiaomin ZHANG,Haiyi JIANG,Yanning ZHANG,Yangyang LIN,Xiuqin RAO. Light stress diagnosis of rapeseed seedling stage based on hyperspectral imaging technology. Journal of Zhejiang University (Agriculture and Life Sciences), 2022, 48(1): 106-116.

链接本文:

https://www.zjujournals.com/agr/CN/10.3785/j.issn.1008-9209.2021.03.081 或 https://www.zjujournals.com/agr/CN/Y2022/V48/I1/106

图1 油菜叶片高光谱图像采集A. Q285高光谱成像系统；B. SOC710 SWIR高光谱成像系统。

图2 450～998 nm（A）和917～1 717 nm（B）的叶片区域和背景区域平均光谱曲线

图3 546和1 121 nm波长图像分割结果A.546 nm灰度图；B.546 nm二值图；C.1 121 nm灰度图； D.1 121 nm二值图。

图4 第1天OD1 组（A）和第10天OD3 组（B）的马氏距离分布

图5 基于光谱反射率显著性分析提取的单生长期光照胁迫敏感波长分布

图6 498～950 nm（A）和939～1 681 nm（B）d1 族的 P 值矩阵灰度图

图7 498～950 nm（A）和939～1 681 nm（B）d1 族的光照胁迫敏感小波系数图

图8 基于CWT提取的全生长期光照胁迫敏感波长分布

图9 498～950 nm（A）和939～1 681 nm（B）波段基于CWT提取的单生长期光照胁迫敏感波长分布

表1 冠层光谱样本集划分

表2 C1～C4特征波长

表3 C1～C4小波系数

表4 通过SPA优选的特征波长在Fisher判别模型下的光照胁迫判别结果

表5 通过CWT-SDA优选的小波特征在Fisher判别模型下的光照胁迫判别结果

图10 939～978 nm波段3阶多项式拟合曲线（A）和切线斜率变化（B）

图11 939～978 nm波段特征tan θ 示意图tan θ =∠M′OA′的正切值。

图12 939～978 nm波段曲线面积（A）和tan θ （B）随观测期的变化趋势

图13 984 nm（A）和1 408 nm（B）反射率随观测期的变化趋势

图14 多特征融合的Fisher判别模型总体分类准确率（A）和OD1 组分类准确率（B）的变化趋势

1	陈莎莎 .长江流域油菜生产规模效益研究[D].武汉:华中农业大学,2017. CHEN S S . The research of rapeseed production scale profit in Yangtze River Basin[D]. Wuhan: Huazhong Agricultural University, 2017. (in Chinese with English abstract)
2	农业部种植业管理司 .全国大宗油料作物生产发展规划(2016—2020年)[J].中国农业信息,2017(1):6-15. DOI:10.1002/wea.2944 Ministry of Agriculture . Planting Industry Management Department. Development plan for national bulk oil crop production (2016—2020)[J]. China Agricultural Information, 2017(1): 6-15. (in Chinese) doi: 10.1002/wea.2944
3	LEE S H , TEWARI R K , HAHN E J , et al . Photon flux density and light quality induce changes in growth, stomatal development, photosynthesis and transpiration of Withania somnifera (L.) Dunal. plantlets[J]. Plant Cell, Tissue and Organ Culture, 2007, 90(2): 141-151. DOI:10.1007/s11240-006-9191-2 doi: 10.1007/s11240-006-9191-2
4	ASKARI-KHORASGANI O , HATTERMAN-VALENTI H , FLORES F B , et al . Managing plant-environment-symbiont interactions to promote plant performance under low temperature stress[J]. Journal of Plant Nutrition, 2019, 42(16): 2010-2027. DOI:10.1080/01904167.2019.1648682 doi: 10.1080/01904167.2019.1648682
5	要旭阳 .光温调控对植物工厂油菜幼苗生长的影响[D].南京:南京农业大学,2016. YAO X Y . Research on the growth of Brassica napus L. seedlings in plant factory based on light and temperature regulation[D]. Nanjing: Nanjing Agricultural University, 2016. (in Chinese with English abstract)
6	YAO X Y , LIU X Y , XU Z G , et al . Effects of light intensity on leaf microstructure and growth of rape seedlings cultivated under a combination of red and blue LEDs[J]. Journal of Integrative Agriculture, 2017, 16(1): 97-105. DOI:10.1016/s2095-3119(16)61393-x doi: 10.1016/s2095-3119(16)61393-x
7	POTTER T I , ROOD S B , ZANEWICH K P . Light intensity, gibberellin content and the resolution of shoot growth in Brassica [J]. Planta, 1999, 207(4): 505-511. DOI:10.1007/s004250050510 doi: 10.1007/s004250050510
8	LEE Y H , KIM K S , JANG Y S , et al . Global gene expression responses to waterlogging in leaves of rape seedlings[J]. Plant Cell Reports, 2014, 33(2): 289-299. DOI:10.1007/s00299-013-1529-8 doi: 10.1007/s00299-013-1529-8
9	王访,廖桂平,王晓乔,等 .基于多重分形理论的油菜缺素叶片特征提取[J].农业工程学报,2013,29(24):181-189. DOI:10.3969/j.issn.1002-6819.2013.24.02 WANG F , LIAO G P , WANG X Q , et al . Feature description for nutrient deficiency rape leaves based on multifractal theory[J]. Transactions of the CSAE, 2013, 29(24): 181-189. (in Chinese with English abstract) doi: 10.3969/j.issn.1002-6819.2013.24.02
10	徐胜勇,林卫国,伍文兵,等 .基于颜色特征的油菜缺素症图像诊断[J].中国油料作物学报,2015,37(4):576-582. DOI:10.7505/j.issn.1007-9084.2015.04.022 XU S Y , LIN W G , WU W B , et al . Nutrient deficiency image diagnose of rapeseed based on color feature[J]. Chinese Journal of Oil Crop Sciences, 2015, 37(4): 576-582. (in Chinese with English abstract) doi: 10.7505/j.issn.1007-9084.2015.04.022
11	张凯兵,章爱群,李春生 .基于HSV空间颜色直方图的油菜叶片缺素诊断[J].农业工程学报,2016,32(19):179-187. DOI:10.11975/j.issn.1002-6819.2016.19.025 ZHANG K B , ZHANG A Q , LI C S . Nutrient deficiency diagnosis method for rape leaves using color histogram on HSV space[J]. Transactions of the CSAE, 2016, 32(19): 179-187. (in Chinese with English abstract) doi: 10.11975/j.issn.1002-6819.2016.19.025
12	SUN Y Y , TONG C , HE S , et al . Identification of nitrogen, phosphorus, and potassium deficiencies based on temporal dynamics of leaf morphology and color[J]. Sustainability, 2018, 10(3): 762. DOI:10.3390/su10030762 doi: 10.3390/su10030762
13	THOMAS S , KUSKA M T , BOHNENKAMP D , et al . Benefits of hyperspectral imaging for plant disease detection and plant protection: a technical perspective[J]. Journal of Plant Diseases and Protection, 2018, 125(1): 5-20. DOI:10.1007/s41348-017-0124-6 doi: 10.1007/s41348-017-0124-6
14	LIU Z Y , QI J G , WANG N N , et al . Hyperspectral discrimination of foliar biotic damages in rice using principal component analysis and probabilistic neural network[J]. Precision Agriculture, 2018, 19(6): 973-991. DOI:10.1007/s11119-018-9567-4 doi: 10.1007/s11119-018-9567-4
15	薛惠云,张永江,刘连涛,等 .干旱胁迫与复水对棉花叶片光谱、光合和荧光参数的影响[J].中国农业科学,2013,46(11):2386-2393. DOI:10.3864/j.issn.0578-1752.2013.11.024 XUE H Y , ZHANG Y J , LIU L T , et al . Responses of spectral reflectance, photosynthesis and chlorophyll fluorescence in cotton during drought stress and rewatering[J]. Scientia Agricultura Sinica, 2013, 46(11): 2386-2393. (in Chinese with English abstract) doi: 10.3864/j.issn.0578-1752.2013.11.024
16	KUSKA M T , BRUGGER A , THOMAS S , et al . Spectral patterns reveal early resistance reactions of barley against Blumeria graminis f. sp. hordei [J]. Phytopathology, 2017, 107(11): 1388-1398. DOI:10.1094/phyto-04-17-0128-r doi: 10.1094/phyto-04-17-0128-r
17	RUMPF T , MAHLEIN A K , STEINER U , et al . Early detection and classification of plant diseases with support vector machines based on hyperspectral reflectance[J]. Computers and Electronics in Agriculture, 2010, 74(1): 91-99. DOI:10.1016/j.compag.2010.06.009 doi: 10.1016/j.compag.2010.06.009
18	IORI A , SCALA V , CESARE D , et al . Hyperspectral and molecular analysis of Stagonospora nodorum blotch disease in durum wheat[J]. European Journal of Plant Pathology, 2015, 141(4): 689-702. DOI:10.1007/s10658-014-0571-x doi: 10.1007/s10658-014-0571-x
19	XIE C Q , SHAO Y N , LI X L , et al . Detection of early blight and late blight diseases on tomato leaves using hyperspectral imaging[J]. Scientific Reports, 2015, 5(1): 1-11. DOI:10.1038/srep16564 doi: 10.1038/srep16564
20	刘燕德,肖怀春,孙旭东,等 .柑橘叶片黄龙病光谱特征选择及检测模型[J].农业工程学报,2018,34(3):180-187. DOI:10.11975/j.issn.1002-6819.2018.03.024 LIU Y D , XIAO H C , SUN X D , et al . Spectral feature selection and discriminant model building for citrus leaf Huanglongbing[J]. Transactions of the CSAE, 2018, 34(3): 180-187. (in Chinese with English abstract) doi: 10.11975/j.issn.1002-6819.2018.03.024
21	MA B D , PU R L , ZHANG S , et al . Spectral identification of stress types for maize seedlings under single and combined stresses[J]. IEEE Access, 2018, 6: 13773-13782. DOI:10.1109/access.2018.2810084 doi: 10.1109/access.2018.2810084
22	GERHARDS M , ROCK G , SCHLERF M , et al . Water stress detection in potato plants using leaf temperature, emissivity, and reflectance[J]. International Journal of Applied Earth Observation and Geoinformation, 2016, 53: 27-39. DOI:10 .1016/j.jag.2016.08.004 doi: 10
23	冷锁虎,夏建飞,胡志中,等 .油菜苗期叶片光合特性研究[J].中国油料作物学报,2002,24(4):10-13. DOI:10.3321/j.issn:1007-9084.2002.04.003 LENG S H , XIA J F , HU Z Z , et al . Studies on photosynthetic characteristics of rapeseed leaves[J]. Chinese Journal of Oil Crop Sciences, 2002, 24(4): 10-13. (in Chinese with English abstract) doi: 10.3321/j.issn:1007-9084.2002.04.003
24	LIU Y N , XU Q Z , LI W C , et al . Long-term high light stress induces leaf senescence in wheat (Triticum aestivum L.)[J]. Photosynthetica, 2019, 57(3): 830-840. DOI:10.32615/ps.2019.086 doi: 10.32615/ps.2019.086
25	TIAN Y L , SACHARZ J , WARE M A , et al . Effects of periodic photoinhibitory light exposure on physiology and productivity of Arabidopsis plants grown under low light[J]. Journal of Experimental Botany, 2017, 68(15): 4249-4262. DOI:10.1093/jxb/erx213 doi: 10.1093/jxb/erx213

[1]	朱乐,赵鑫泽,蒋立希. 甘蓝型油菜钾离子转运载体HAK/KUP/KT家族的全基因组鉴定与分析[J]. 浙江大学学报（农业与生命科学版）, 2021, 47(3): 303-313.
[2]	MAODZEKA Antony, 卢凌志, 赵鑫泽, 徐颖, 吴德志, 蒋立希. 外源5-氨基乙酰丙酸对油菜幼苗硫代葡萄糖苷生物合成的影响(英文)[J]. 浙江大学学报（农业与生命科学版）, 2020, 46(3): 291-307.
[3]	张萌,李光辉. 基于RELIEF算法和极限学习机的苹果轻微损伤高光谱检测方法[J]. 浙江大学学报（农业与生命科学版）, 2019, 45(1): 126-134.
[4]	伍文杰, 吴崇友. 油菜联合收割机的割台参数优化[J]. 浙江大学学报（农业与生命科学版）, 2018, 44(4): 481-489.
[5]	严涛, 玄立杰, 蒋立希. 类黄酮对油菜脂肪酸积累的影响及其分子机制[J]. 浙江大学学报（农业与生命科学版）, 2018, 44(3): 253-258.
[6]	温雪玮，陈泱泱，武斌，胡东维，梁五生. 以防治油菜菌核病为目标的核盘菌乙酰乳酸合酶抑制剂的筛选[J]. 浙江大学学报(农业与生命科学版), 2017, 43(5): 589-598.
[7]	王昊一，李宇玲，朱乐，蒋立希. 油菜种子休眠性对脂肪酸积累的影响及其分子机制[J]. 浙江大学学报(农业与生命科学版), 2017, 43(4): 397-403.
[8]	王舒甜，王金平，张金池，岳健敏. 油菜素内酯对盐胁迫下香樟幼苗叶片抗氧化酶活性的影响[J]. 浙江大学学报(农业与生命科学版), 2017, 43(4): 476-482.
[9]	倪西源，黄吉祥，柳寒，潘兵，赵坚义. 应用分子标记辅助选育甘蓝型油菜杂交种的可行性[J]. 浙江大学学报(农业与生命科学版), 2017, 43(2): 173-182.
[10]	刘于, 王伏林, 刘仁虎. 甘蓝型油菜黄籽突变对含油量和蛋白质含量的影响[J]. 浙江大学学报(农业与生命科学版), 2017, 43(1): 37-44.
[11]	王舒甜, 张金池, 张亮. 油菜素内酯对盐胁迫下香樟叶片光合色素以及叶绿素荧光的影响[J]. 浙江大学学报(农业与生命科学版), 2017, 43(1): 45-53.
[12]	王继变1,2，陈洪成2，3，陈玉波2,4，张晓玉2,4，徐海明1，赵坚义2. 通过条件QTL定位分析油菜不同脂肪酸组分对含油量性状的影响*[J]. 浙江大学学报(农业与生命科学版), 2013, 39(5): 504-5125.
[13]	冯雷，张德荣，陈双双，冯斌，谢传奇，陈佑源，何勇. 基于高光谱成像技术的茄子叶片灰霉病早期检测[J]. 浙江大学学报(农业与生命科学版), 2012, 38(3): 311-317.
[14]	倪西源,徐小栋,黄吉祥,陈飞,周伟军,赵坚义. 利用分子标记辅助选育油菜隐性上位互作核不育临保系[J]. 浙江大学学报(农业与生命科学版), 2011, 37(4): 407-412.
[15]	周康韵,楼庆庆,洪小龙,裘正军,应霞芳. 浙江省油菜全程机械化生产规范及实践[J]. 浙江大学学报(农业与生命科学版), 2011, 37(3): 295-299.

Viewed

Full text

Abstract

Cited

Shared

Discussed